Bioinformatics and Personalized Medicine

Nicholas A. Shackel

1 A.W. Morrow Gastroenterology and Liver Centre Royal Prince Alfred Hospital

2 Liver Laboratory, Centenary InstituteSydney, NSW, Australia

3 Medicine University of SydneySydney, NSW, Australia.

Overview

• Genome / Transcriptome

• Understanding disease

– mRNA Expression

– miRNA Expression

• Personalised medicine

• New technologies

3

Bioinformatics

• A long term goal of Bioinformatics is to discover the causal processes among genes, proteins, and other molecules in cells

• This can be achieved by using data from High Throughput experiments, such as microarrays, deep-sequencing and proteomics

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Functional Genomics

Cell Nucleus

Chromosome

Protein

Graphics courtesy of the National Human Genome Research Institute

Gene (DNA)Gene (mRNA), single strand

Systems Biology

New Paradigm

“ The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration..."

(Sauer Science April 2007)

Genome

• 3 billion bases (x2)

• 1.5% protein encoding = 23,000 unique proteins

• >100,000 alternate splicing and post translation protein variants

• 1.5-8% of the genome has regulatory elements– UTRs, Promoters etc

• Single Nucleotide Polymorphism (SNP) 1:100 – 1:1000

• 90% “Junk” DNA– Unrecognized regulatory elements?– Entropy rate for coding and non-coding regions different

• Transcription without translation

Transcriptomes

• Total transcriptome (mRNA pool)– SAGE ~ 100 000 (www.sagenet.org)

– UniGene 86 820 (Build 193)

• Organ transcriptomes (Velculescu et. al. 1999 Nature Genetics 23 p387)

– Brain - 46 %

– Liver - 26 %• “Liverpool” Liver array (Coulouarn et al. 2004 Hepatology 39 p353)

– 12638 transcripts

• Normal colon – 32% -> Diseased colon - 50%

• Understanding the liver transcriptome (Anderson et. al. 1997 Electrophoresis 18 p533)

– Secreted and abundant transcripts over represented in mRNA (29/50 mRNA vs. 0/50 protein)

• Cell transcriptomes 5000 to > 15000 genes (lymphocyte ~ 12 000 genes)

Gene Regulation and Expression

Post Translational Mechanisms

Alternate Splicing / ncRNA

Epigenetic regulation

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Understanding Disease

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HCC Pathogenesis

Saffroy (2007) Clin Chem Lab Med 45(9): 1169

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HCV Genotype 1 vs Genotype 3 Clustering

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Gene Expression and Outcome

in HCC

Hoshida (2008)NEJM: 1

Chromosomal Aberrations

Pie (2009) Acta Biochim Biophys Sin: 1

mRNA profiling of HCV-induced cirrhosis and HCC - Hierarchal Clustering

HCC Cirrhotic (F4)

Donor

Cir

rhoti

c G

1

HC

C G

1H

CC

G3

HC

C G

1C

irrh

oti

c G

3C

irrh

oti

c G

3H

CC

G_

HC

C G

_+H

BV

HC

C G

3H

CC

ALD

HC

C G

3H

CC

ALD

HC

C A

LDH

CC

G3

Cir

rhoti

c G

1C

irrh

oti

c G

1H

CC

G1

Cir

rhoti

c G

3C

irrh

oti

c G

3H

CC

G4

+H

BV

Cir

rhoti

c G

1H

CC

G1

Cir

rhoti

c G

3C

irrh

oti

c G

1C

irrh

oti

c G

3H

CC

G1

HC

C G

3C

irrh

oti

c A

LDC

irrh

oti

c A

LDC

irrh

oti

c A

LDC

irrh

oti

c A

LDC

irrh

oti

c A

LDD

onor

Donor

Donor

Donor

Pearson’s Correlation

HCC Pathogenesis

Aravalli (2008) Hepatology: 2049

MicroRNA Targets

Chen WJG 2009 p1665

LIVER

miRNA Clinical

Outcomes

Junfang et al NEJM 2009 p1437

Pearson’s Correlation

• Segregation is based on grade and cause of injury

• Donor < Low fibrosis < Severe fibrosis/Cirrhotic < HCC

• HCV vs ALD

Donor

Donor

Donor

G3

F0

Expla

nt

G1

F4

G3

F3

G1

F4

Expla

nt

G3

HC

C

Expla

nt

G1

F4

Expla

nt

G1

F4

Expla

nt

G3

F4

G1

F3

Expla

nt

G1

F4

Expla

nt

G3

F4

Expla

nt

G3

F4

Expla

nt

G1

HC

CExpla

nt

G3

H

CC

Expla

nt

G3

H

CC

Expla

nt

G1

HC

C

Expla

nt

G1

HC

CExpla

nt

G3

F4

ALD

ALD

ALD

Expla

nt

G3

HC

CA

LD

Expla

nt

G1

HC

C

Donor

G1

F0

G1

F0

G3

F2

G3

F1

G3

F2

G1

F4

G1

F0

G3

F1

G1

F4

G3

F3

Donor

Low Fibrosis

Severe fibrosis/ Cirrhotic

HCC ALD

miRNA profiling of HCV-induced fibrosis, cirrhosis and HCC

Hierarchal Clustering

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Personalised Medicine

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Interleukin-28B

1671 Patients from IDEAL

19q13.13

Rapidly confirmed• Australia Group• Japanese Group• European Group

Ge et al Nature 2009 461, p1

IL-28B

Ge et al Nature 2009 461, p1

IL-28B

Ge et al Nature 2009 461, p1

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New Technologies

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Sequencing costs

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Deep Sequencing Technologies

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Summary

• Genomics methods have already lead to personalized medicine– Warfarin therapy– Hepatitis C Treatment responses– Malignancy

• Deep Sequencing presents a “deluge” of data– Promise of personalised medicine– Analysis problems dramatically amplified